World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
55
Citations
16672
World Ranking
4215
National Ranking
563

Lequan Yu publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Lequan Yu sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 114 publications — 13th percentile

13% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Lequan Yu D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Lequan Yu sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 55 D-Index — 71st percentile

71% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Lequan Yu is affiliated with the University of Hong Kong in China and has contributed extensively to research in computer science and medicine, with a primary focus on medical imaging and artificial intelligence applications.

Their work covers a range of topics including:

  • Radiomics and Machine Learning in Medical Imaging
  • Advanced Neural Network Applications
  • AI in Cancer Detection
  • Domain Adaptation and Few-Shot Learning
  • Medical Image Segmentation Techniques
  • COVID-19 Diagnosis Using AI
  • Medical Imaging Techniques and Applications

Lequan Yu has a substantial publication record in prominent venues such as:

  • arXiv (Cornell University)
  • IEEE Transactions on Medical Imaging
  • Medical Image Analysis
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • IEEE Journal of Biomedical and Health Informatics

Among their recent papers are:

  • Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation, 2020, IEEE Transactions on Neural Networks and Learning Systems
  • nnFormer: Volumetric Medical Image Segmentation via a 3D Transformer, 2023, IEEE Transactions on Image Processing
  • nnFormer: Interleaved Transformer for Volumetric Segmentation, 2021, arXiv (Cornell University)
  • Uncertainty-aware Multi-view Co-training for Semi-supervised Medical Image Segmentation and Domain Adaptation, 2020, Medical Image Analysis
  • DoFE: Domain-Oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets, 2020, IEEE Transactions on Medical Imaging

Frequent collaborators in their research include:

  • Lei Xing (32 coauthored works)
  • Pheng-Ann Heng (25 coauthored works)
  • Shujun Wang (19 coauthored works)
  • Liansheng Wang (17 coauthored works)
  • Weiqin Zhao (15 coauthored works)

Their research spans significant subfields such as:

  • Computer Vision and Pattern Recognition
  • Artificial Intelligence
  • Radiology, Nuclear Medicine and Imaging
  • Biomedical Engineering
  • Pulmonary and Respiratory Medicine

Lequan Yu's interdisciplinary work bridges technical and medical domains, reflecting a focus on the development and application of machine learning techniques for medical image analysis and diagnostic processes.

Best Publications

  • Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks

    Lequan Yu;Hao Chen;Qi Dou;Jing Qin

  • Uncertainty-Aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation

    Lequan Yu;Shujun Wang;Xiaomeng Li;Chi Wing Fu

  • VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images

    Hao Chen;Qi Dou;Lequan Yu;Jing Qin

  • Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks

    Qi Dou;Hao Chen;Lequan Yu;Lei Zhao

  • DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation

    Hao Chen;Xiaojuan Qi;Lequan Yu;Pheng-Ann Heng

  • PU-Net: Point Cloud Upsampling Network

    Lequan Yu;Xianzhi Li;Chi-Wing Fu;Daniel Cohen-Or

  • 3D deeply supervised network for automated segmentation of volumetric medical images.

    Qi Dou;Lequan Yu;Hao Chen;Yueming Jin

  • Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection

    Qi Dou;Hao Chen;Lequan Yu;Jing Qin

  • nnFormer: Volumetric Medical Image Segmentation via a 3D Transformer

    Unknown

  • DCAN: Deep contour-aware networks for object instance segmentation from histology images

    Hao Chen;Xiaojuan Qi;Lequan Yu;Qi Dou

  • Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation

    Xiaomeng Li;Lequan Yu;Hao Chen;Chi-Wing Fu

  • MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data

    Quande Liu;Qi Dou;Lequan Yu;Pheng Ann Heng

  • Semi-Supervised Medical Image Classification With Relation-Driven Self-Ensembling Model

    Quande Liu;Lequan Yu;Luyang Luo;Qi Dou

  • Comparative Validation of Polyp Detection Methods in Video Colonoscopy: Results From the MICCAI 2015 Endoscopic Vision Challenge

    Jorge Bernal;Nima Tajkbaksh;Francisco Javier Sanchez;Bogdan J. Matuszewski

  • CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading

    Xiaomeng Li;Xiaowei Hu;Lequan Yu;Lei Zhu

  • Volumetric convnets with mixed residual connections for automated prostate segmentation from 3d MR images

    Lequan Yu;Xin Yang;Hao Chen;Jing Qin

  • 3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes

    Qi Dou;Hao Chen;Yueming Jin;Lequan Yu

  • SV-RCNet: Workflow Recognition From Surgical Videos Using Recurrent Convolutional Network

    Yueming Jin;Qi Dou;Hao Chen;Lequan Yu

  • Patch-Based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentation

    Shujun Wang;Lequan Yu;Xin Yang;Chi-Wing Fu

  • EC-Net: An Edge-Aware Point Set Consolidation Network

    Lequan Yu;Xianzhi Li;Chi-Wing Fu;Daniel Cohen-Or

  • Integrating Online and Offline Three-Dimensional Deep Learning for Automated Polyp Detection in Colonoscopy Videos

    Lequan Yu;Hao Chen;Qi Dou;Jing Qin

  • Transformation Consistent Self-ensembling Model for Semi-supervised Medical Image Segmentation

    Xiaomeng Li;Lequan Yu;Hao Chen;Chi-Wing Fu

Frequent Co-Authors

Pheng-Ann Heng
Pheng-Ann Heng Chinese University of Hong Kong
Chi-Wing Fu
Chi-Wing Fu Chinese University of Hong Kong
Qi Dou
Qi Dou Chinese University of Hong Kong
Jing Qin
Jing Qin Hong Kong Polytechnic University
Xin Yang
Xin Yang Sun Yat-sen University
Hao Chen
Hao Chen Chinese University of Hong Kong
Lei Xing
Lei Xing Stanford University
Dong Ni
Dong Ni Shenzhen University
Daniel Cohen-Or
Daniel Cohen-Or Tel Aviv University
Vincent Mok
Vincent Mok Chinese University of Hong Kong

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